Speech-to-text conversion and text-to-speech conversion are two very commonly used techniques to improve man-machine interface with numerous real world applications. A lot of advancements have taken place to improve the accuracy of these techniques. However, despite all these advancements, when existing speech recognition (i.e., speech-to-text conversion) techniques are applied, the recording device (e.g., microphone) captures lots of background noise in the speech. This results in loss of words and/or misinterpretation of words, thereby causing overall decline in accuracy and reliability of speech recognition. Even the most sophisticated speech-to-text conversion algorithms are able to achieve accuracies only up to 80 percent.
This lack of accuracy and reliability of existing speech recognition techniques in turn hamper the reliability of the applications employing the speech recognition techniques. Such inaccuracy may also compromise the security of critical applications which may not be able to differentiate between false positives and false negatives, and hence may not be able to prevent a fraud. It is therefore desirable to provide an efficient technique that reduces errors and therefore improves accuracy of the speech-to-text conversions.